2.1 Install Packages

# load packages, installing if missing
if (!require(librarian)){
  install.packages("librarian")
  library(librarian)
}

## Loading required package: librarian

librarian::shelf(
  dismo, dplyr, DT, ggplot2, here, htmltools, leaflet, mapview, purrr, raster, readr, rgbif, rgdal, rJava, sdmpredictors, sf, spocc, tidyr)

## 
##   The 'cran_repo' argument in shelf() was not set, so it will use
##   cran_repo = 'https://cran.r-project.org' by default.
## 
##   To avoid this message, set the 'cran_repo' argument to a CRAN
##   mirror URL (see https://cran.r-project.org/mirrors.html) or set
##   'quiet = TRUE'.

select <- dplyr::select # overwrite raster::select
options(readr.show_col_types = FALSE)

# set random seed for reproducibility
set.seed(42)

# directory to store data
dir_data <- here("data/sdm")
dir.create(dir_data, showWarnings = F, recursive = T)

2.3 Get Species Observations

obs_csv <- file.path(dir_data, "obs.csv")
obs_geo <- file.path(dir_data, "obs.geojson")
redo    <- FALSE

if (!file.exists(obs_geo) | redo){
  # get species occurrence data from GBIF with coordinates
  (res <- spocc::occ(
    query = 'Haliaeetus leucocephalus', 
    from = 'gbif', has_coords = T, limit = 10000))
  
  # extract data frame from result
  df <- res$gbif$data[[1]] 
  readr::write_csv(df, obs_csv)
  
  # convert to points of observation from lon/lat columns in data frame
  obs <- df %>% 
    sf::st_as_sf(
      coords = c("longitude", "latitude"),
      crs = st_crs(4326)) %>% 
    select(prov, key) # save space (joinable from obs_csv)
  sf::write_sf(obs, obs_geo, delete_dsn=T)
}
obs <- sf::read_sf(obs_geo)
nrow(obs) # number of rows

## [1] 10000

# show points on map
mapview::mapview(obs, map.types = "Esri.WorldPhysical")

2.4 Get Environmental Data

2.4.1 Presence

dir_env <- file.path(dir_data, "env")

# set a default data directory
options(sdmpredictors_datadir = dir_env)

# choosing terrestrial
env_datasets <- sdmpredictors::list_datasets(terrestrial = TRUE, marine = FALSE)

# show table of datasets
env_datasets %>% 
  select(dataset_code, description, citation) %>% 
  DT::datatable()

# choose datasets for a vector
env_datasets_vec <- c("WorldClim", "ENVIREM")

# get layers
env_layers <- sdmpredictors::list_layers(env_datasets_vec)
DT::datatable(env_layers)

# choose layers after some inspection and perhaps consulting literature
env_layers_vec <- c("WC_alt", "WC_bio1", "WC_bio2", "ER_tri", "ER_topoWet")

# get layers
env_stack <- load_layers(env_layers_vec)

# interactive plot layers, hiding all but first (select others)
# mapview(env_stack, hide = T) # makes the html too big for Github
plot(env_stack, nc=2)

# crop the environmental rasters to a reasonable study area around our species observations
obs_hull_geo  <- file.path(dir_data, "obs_hull.geojson")
env_stack_grd <- file.path(dir_data, "env_stack.grd")

if (!file.exists(obs_hull_geo) | redo){
  # make convex hull around points of observation
  obs_hull <- sf::st_convex_hull(st_union(obs))
  
  # save obs hull
  write_sf(obs_hull, obs_hull_geo)
}
obs_hull <- read_sf(obs_hull_geo)

# show points on map
mapview(
  list(obs, obs_hull))

if (!file.exists(env_stack_grd) | redo){
  obs_hull_sp <- sf::as_Spatial(obs_hull)
  env_stack <- raster::mask(env_stack, obs_hull_sp) %>% 
    raster::crop(extent(obs_hull_sp))
  writeRaster(env_stack, env_stack_grd, overwrite=T)  
}
env_stack <- stack(env_stack_grd)

# show map
# mapview(obs) + 
#   mapview(env_stack, hide = T) # makes html too big for Github
plot(env_stack, nc=2)

2.4.2 Pseudo-Absence

absence_geo <- file.path(dir_data, "absence.geojson")
pts_geo     <- file.path(dir_data, "pts.geojson")
pts_env_csv <- file.path(dir_data, "pts_env.csv")

if (!file.exists(absence_geo) | redo){
  # get raster count of observations
  r_obs <- rasterize(
    sf::as_Spatial(obs), env_stack[[1]], field=1, fun='count')
  
  # show map
  # mapview(obs) + 
  #   mapview(r_obs)
  
  # create mask for 
  r_mask <- mask(env_stack[[1]] > -Inf, r_obs, inverse=T)
  
  # generate random points inside mask
  absence <- dismo::randomPoints(r_mask, nrow(obs)) %>% 
    as_tibble() %>% 
    st_as_sf(coords = c("x", "y"), crs = 4326)
  
  write_sf(absence, absence_geo, delete_dsn=T)
}
absence <- read_sf(absence_geo)

# show map of presence, ie obs, and absence
mapview(obs, col.regions = "green") + 
  mapview(absence, col.regions = "gray")

## Warning in cbind(`Feature ID` = fid, mat): number of rows of result is not a
## multiple of vector length (arg 1)

if (!file.exists(pts_env_csv) | redo){

  # combine presence and absence into single set of labeled points 
  pts <- rbind(
    obs %>% 
      mutate(
        present = 1) %>% 
      select(present, key),
    absence %>% 
      mutate(
        present = 0,
        key     = NA)) %>% 
    mutate(
      ID = 1:n()) %>% 
    relocate(ID)
  write_sf(pts, pts_geo, delete_dsn=T)

  # extract raster values for points
  pts_env <- raster::extract(env_stack, as_Spatial(pts), df=TRUE) %>% 
    tibble() %>% 
    # join present and geometry columns to raster value results for points
    left_join(
      pts %>% 
        select(ID, present),
      by = "ID") %>% 
    relocate(present, .after = ID) %>% 
    # extract lon, lat as single columns
    mutate(
      #present = factor(present),
      lon = st_coordinates(geometry)[,1],
      lat = st_coordinates(geometry)[,2]) %>% 
    select(-geometry)
  write_csv(pts_env, pts_env_csv)
}
pts_env <- read_csv(pts_env_csv)

pts_env %>% 
  # show first 10 presence, last 10 absence
  slice(c(1:10, (nrow(pts_env)-9):nrow(pts_env))) %>% 
  DT::datatable(
    rownames = F,
    options = list(
      dom = "t",
      pageLength = 20))

nrow(pts_env)

## [1] 20000

datatable(pts_env, rownames = F)

Term Plots

pts_env %>% 
  select(-ID) %>% 
  mutate(
    present = factor(present)) %>% 
  pivot_longer(-present) %>% 
  ggplot() +
  geom_density(aes(x = value, fill = present)) + 
  scale_fill_manual(values = alpha(c("gray", "green"), 0.5)) +
  scale_x_continuous(expand=c(0,0)) +
  scale_y_continuous(expand=c(0,0)) +
  theme_bw() + 
  facet_wrap(~name, scales = "free") +
  theme(
    legend.position = c(1, 0),
    legend.justification = c(1, 0))

## Warning: Removed 147 rows containing non-finite values (stat_density).

## Pairs plot to show correlations between variables

GGally::ggpairs(
  select(pts_env, -ID),
  aes(color = factor(present)))

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 60 rows containing missing values

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 15 rows containing missing values

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 66 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 28 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 66 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 28 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 66 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 28 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 24 rows containing missing values

## Warning: Removed 60 rows containing missing values (geom_point).

## Warning: Removed 66 rows containing missing values (geom_point).

## Warning: Removed 66 rows containing missing values (geom_point).

## Warning: Removed 66 rows containing missing values (geom_point).

## Warning: Removed 60 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 60 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 60 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 60 rows containing missing values

## Warning: Removed 15 rows containing missing values (geom_point).

## Warning: Removed 28 rows containing missing values (geom_point).

## Warning: Removed 28 rows containing missing values (geom_point).

## Warning: Removed 28 rows containing missing values (geom_point).

## Warning: Removed 60 rows containing missing values (geom_point).

## Warning: Removed 15 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 15 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 15 rows containing missing values

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 60 rows containing missing values (geom_point).

## Warning: Removed 15 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 60 rows containing missing values (geom_point).

## Warning: Removed 15 rows containing missing values (geom_point).

# Setup Data - Drop rows with any NAs - remove terms we don’t want to model - use a simplified formula _present_ ~. to predict where the species is present based on all other fields in the data from (ie. y ~ X1 + X2 + …Xn)

d <- pts_env %>% 
  select(-ID) %>% # remove terms we don't want to model 
  tidyr::drop_na() # drop the rows with NA values
nrow(d)

## [1] 19934

Linear Model

# fit a linear model
mdl_linear <- lm(present ~ ., data = d)
summary(mdl_linear)

## 
## Call:
## lm(formula = present ~ ., data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.28636 -0.33804  0.04561  0.35671  1.32955 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.762e-01  1.111e-01   3.388 0.000707 ***
## WC_alt       1.445e-04  1.195e-05  12.088  < 2e-16 ***
## WC_bio1      6.831e-02  2.230e-03  30.636  < 2e-16 ***
## WC_bio2     -6.595e-02  2.071e-03 -31.844  < 2e-16 ***
## ER_tri      -2.455e-03  1.724e-04 -14.238  < 2e-16 ***
## ER_topoWet  -5.597e-02  3.489e-03 -16.045  < 2e-16 ***
## lon          6.501e-03  2.897e-04  22.442  < 2e-16 ***
## lat          3.724e-02  1.976e-03  18.840  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4322 on 19926 degrees of freedom
## Multiple R-squared:  0.2532, Adjusted R-squared:  0.253 
## F-statistic: 965.3 on 7 and 19926 DF,  p-value: < 2.2e-16

y_predict <- predict(mdl_linear, d, type="response")
y_true <- d$present
range(y_predict)

## [1] -0.329554  1.286362

range(y_true)

## [1] 0 1

Generalized Linear Model

# fit a generalized linear model with a binomial logit link function
mdl_glm <- glm(present ~ ., family = binomial(link="logit"), data = d)
summary(mdl_glm)

## 
## Call:
## glm(formula = present ~ ., family = binomial(link = "logit"), 
##     data = d)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7969  -0.8593  -0.1855   0.9010   2.9607  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.763e+00  5.985e-01  -2.945  0.00323 ** 
## WC_alt       7.552e-04  6.628e-05  11.394  < 2e-16 ***
## WC_bio1      3.629e-01  1.250e-02  29.038  < 2e-16 ***
## WC_bio2     -3.241e-01  1.199e-02 -27.020  < 2e-16 ***
## ER_tri      -1.305e-02  9.681e-04 -13.477  < 2e-16 ***
## ER_topoWet  -2.757e-01  1.888e-02 -14.602  < 2e-16 ***
## lon          3.354e-02  1.583e-03  21.183  < 2e-16 ***
## lat          2.077e-01  1.088e-02  19.087  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 27634  on 19933  degrees of freedom
## Residual deviance: 21989  on 19926  degrees of freedom
## AIC: 22005
## 
## Number of Fisher Scoring iterations: 4

y_predict_glm <- predict(mdl_glm, d, type = "response")
range(y_predict_glm)

## [1] 0.01209068 0.97998789

Look at the terms plots to see the relationship between predictor and response

# show term plots
termplot(mdl_glm, partial.resid = TRUE, se = TRUE, main = F, ylim = "free")

## Generalize Additive Model With a general additive model we can add “wiggle” to the relationship between predictor and response by introducing smooth s() terms

librarian::shelf(mgcv)

## 
##   The 'cran_repo' argument in shelf() was not set, so it will use
##   cran_repo = 'https://cran.r-project.org' by default.
## 
##   To avoid this message, set the 'cran_repo' argument to a CRAN
##   mirror URL (see https://cran.r-project.org/mirrors.html) or set
##   'quiet = TRUE'.

# fit a generalized additive model with smooth predictors
mdl_gen_add <- mgcv::gam(
  formula = present ~ s(WC_alt) + s(WC_bio1) + s(WC_bio2) + s(ER_tri) + s(ER_topoWet) + s(lon) + s(lat),
  family = binomial, data = d)
summary(mdl_gen_add)

## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## present ~ s(WC_alt) + s(WC_bio1) + s(WC_bio2) + s(ER_tri) + s(ER_topoWet) + 
##     s(lon) + s(lat)
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.1347     0.0357  -3.772 0.000162 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                 edf Ref.df Chi.sq p-value    
## s(WC_alt)     8.762  8.979 490.73  <2e-16 ***
## s(WC_bio1)    7.950  8.455 349.27  <2e-16 ***
## s(WC_bio2)    8.767  8.980 462.99  <2e-16 ***
## s(ER_tri)     8.772  8.984 112.77  <2e-16 ***
## s(ER_topoWet) 8.454  8.894  73.74  <2e-16 ***
## s(lon)        7.486  8.449 163.18  <2e-16 ***
## s(lat)        8.872  8.992 229.68  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.441   Deviance explained = 38.8%
## UBRE = -0.14616  Scale est. = 1         n = 19934

# show term plot
plot(mdl_gen_add, scale=0)

species

Marie Rivers

1/5/2022